Title:

A REVIEW OF MACHINE LEARNING ALGORITHMS FOR ACCURATE LOAD FORECASTING IN SMART GRID ENVIRONMENTS

Author:

Nagraj Pralhad Kamble and Dr. Nilesh Vasant Ingale

Abstract:

Accurate load forecasting is a critical component of smart grid management, enabling efficient energy generation, demand response, stability, and economic operation. With the integration of renewable energy sources, distributed generation, and advanced metering infrastructure, traditional statistical methods have become insufficient for handling nonlinear and high-dimensional load patterns. Machine learning algorithms have emerged as powerful tools for improving forecasting accuracy in short-term, medium-term, and long-term load prediction. This review paper examines classical machine learning techniques, deep learning approaches, hybrid models, and ensemble strategies applied in smart grid load forecasting. The comparative strengths, limitations, and performance metrics of various algorithms are discussed. A comprehensive table summarizing commonly used algorithms and their characteristics is provided.

Keyword:

Smart Grid, Load Forecasting, Machine Learning, Deep Learning.